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QoS-Aware Dynamic Composition of Web Services Using Numerical Temporal Planning

机译:使用数字时间规划的可感知QoS的Web服务动态组合

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Web service composition (WSC) is the task of combining a chain of connected single services together to create a more complex and value-added composite service. Quality of service (QoS) has been mostly applied to represent nonfunctional properties of web services and differentiate those with the same functionality. Many research has been done on QoS-aware service composition, as it significantly affects the quality of a composite service. However, existing methods are restricted to predefined workflows, which can incur a couple of limitations, including the lack of guarantee for the optimality on overall QoS and for the completeness of finding a composite service solution. In this paper, instead of predefining a workflow model for service composition, we propose a novel planning-based approach that can automatically convert a QoS-aware composition task to a planning problem with temporal and numerical features. Furthermore, we use state-of-the-art planners, including an existing one and a self-developed one, to handle complex temporal planning problems with logical reasoning and numerical optimization. Our approach can find a composite service graph with the optimal overall QoS value while satisfying multiple global QoS constraints. We implement a prototype system and conduct extensive experiments on large web service repositories. The experimental results show that our proposed approach largely outperforms existing ones in terms of solution quality and is efficient enough for practical deployment.
机译:Web服务组合(WSC)是将一系列连接的单个服务组合在一起以创建更复杂和增值的复合服务的任务。服务质量(QoS)大部分已用于表示Web服务的非功能属性,并区分具有相同功能的服务。已经对QoS感知服务组合进行了许多研究,因为它显着影响组合服务的质量。但是,现有方法仅限于预定义的工作流,这可能会带来一些限制,包括缺乏对总体QoS的最优性以及寻找复合服务解决方案的完整性的保证。在本文中,我们没有预定义用于服务组合的工作流模型,而是提出了一种基于计划的新颖方法,该方法可以将QoS感知的组合任务自动转换为具有时间和数字特征的计划问题。此外,我们使用最先进的计划程序(包括现有计划程序和自行开发的计划程序)通过逻辑推理和数值优化来处理复杂的时间计划问题。我们的方法可以找到具有最佳总体QoS值,同时满足多个全局QoS约束的复合服务图。我们实施了一个原型系统,并对大型Web服务存储库进行了广泛的实验。实验结果表明,我们提出的方法在解决方案质量方面大大优于现有方法,并且对于实际部署而言足够有效。

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